Dual-band multi-beam reconfigurable terahertz antenna based on graphene frequency selective surface

In this paper,a dual-band graphene-based frequency selective surface(GFSS)is investigated and the operating mechanism of this GFSS is analyzed.By adjusting the bias voltage to control the graphene chemical po-tential between 0 eV and 0.5 eV,the GFSS can achieve four working states:dual-band passband,high-pass lowimpedance,low-pass high-impedance,and band-stop.Based on this GFSS,a hexagonal radome on a broadband omnidirectional monopole antenna is proposed,which can achieve independent 360°six-beam omnidirectional scanning at 1.08 THz and 1.58 THz dual bands.In addition,while increasing the directionality,the peak gains of the dual bands reach 7.44 dBi and 6.67 dBi,respectively.This work provides a simple method for realizing multi-band terahertz multi-beam reconfigurable antennas.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network

Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimization(PPO) and long short-term memory(LSTM).

Performance Analysis in SWIPT-Based Bidirectional D2D Communications in Cellular Networks

In this paper,a novel traffic-aware cooperative cognitive radio network that can enable deviceto-device(D2D)communications in cellular system is proposed and investigated.By providing relay cooperation to cellular transmission,D2D users can realize their own two-way communication in the licensed spectrum.Unlike most existing works,in the proposed network,both wireless-powered D2D users can harvest energy via radio-frequency signals received from basic station(BS)through a hybrid protocol which can adaptively utilize both time-switching and powersplitting techniques.Specifically,D2D users perform decode-and-forward operation to transmit signals,and mobile user(MU)employs a selection combining technique.In addition,the performance of both D2D system and cellular system in the proposed network is evaluated by deriving the expressions of their exact outage probability and throughput.Numerical and simulation results validate correctness of derivations and reveal the influence of various system parameters of the proposed network.

Reducing congestion and emissions via roadside unit deployment under mixed traffic flow

It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efciently.Numerical results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.

DM Code Key Point Detection Algorithm Based on CenterNet

Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.

Traffic assignment problem under tradable credit scheme in a bi-modal stochastic transportation network: A cumulative prospect theory approach

The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.

V2V Channel Modeling at 5.2 GHz for Highway Environment

To design and evaluate vehicle-to-vehicle(V2V)communication systems in intelligent transportation system(ITS),it is important to understand the propagation mechanisms and channel models of V2V channels.This paper aims to analyze the channel models at 5.2 GHz for the highway environment in obstructed line-of-sight(OLoS)and line-of-sight(LoS)scenarios,particularly the vehicle connectivity probability derivation based on the propagation model obtained from measurement.First,the path loss(PL),shadow fading(SF),narrowband K-factor,and small-scale amplitude fading are analyzed.Results showed that the received signal magnitude follows Rice and Weibull distribution in LoS and OLoS scenarios,respectively.Second,we develop simple and low-complexity tapped delay line(TDL)models with a 10 MHz bandwidth for LoS and OLoS scenarios;in addition,we investigate the wideband K-factor,the root mean square delay spread(RMS-DS),and delay-Doppler spectrum.Third,we derive the closed form connectivity probability between any two vehicles in the presence of Weibull fading channel,and analyze the effects of Weibull fading channel and traffic parameters on connectivity.It is found that Weibull fading parameter,transmit power and vehicle density have positive impact on connectivity probability,PL exponent has negative impact on connectivity probability.

Dual information hiding algorithm based on the regularity of 3D mesh model

Aiming at the problems of low embedding capacity and inflexibility of embedded information in current three-dimensional(3 D) model information hiding technology, a dual information hiding algorithm based on the mesh characteristics of 3 D model is proposed. The algorithm adopts the strategy of double embedding. By analyzing the regularity of each region of the 3 D model, the feature regions with higher regularity are extracted for embedding secret information. First, in these feature areas, the first secret information is embedded by changing the order of the face list of the object(OBJ) file of the 3 D model. Secondly, filter the triangular meshes according to the regularity, calculate the angle between the plane normals of the two adjacent triangular meshes where the two vertices are adjacent, use the discrete cosine transform to process the angle sequence, and secret information is embedded in the transformation coefficients. The experimental analysis shows that the algorithm can significantly improve the embedding capacity and robustness, and it can effectively resist severe shear attack, geometric attack, and a certain degree of noise attack.

A Novel Aquila Optimizer Based PV Array Reconfiguration Scheme to Generate Maximum Energy under Partial Shading Condition

This paper develops a real-time PV arrays maximum power harvesting scheme under partial shading condition(PSC)by reconfiguring PV arrays using Aquila optimizer(AO).AO is based on the natural behaviors of Aquila in capturing prey,which can choose the best hunting mechanism ingeniously and quickly by balancing the local exploitation and global exploration via four hunting methods of Aquila:choosing the searching area through high soar with the vertical stoop,exploring in different searching spaces through contour flight with quick glide attack,exploiting in convergence searching space through low flight with slow attack,and swooping through walk and grabbing prey.In general,PV arrays reconfiguration is a problem of discrete optimization,thus a series of discrete operations are adopted in AO to enhance its optimization performance.Simulation results based on 10 cases under PSCs show that the mismatched power loss obtained by AO is the smallest compared with genetic algorithm,particle swarm optimization,ant colony algorithm,grasshopper optimization algorithm,and butterfly optimization algorithm,which reduced by 4.34%against butterfly optimization algorithm.

Research on the Comparison between the Different Policies by Service Level and Inventory Level Performance of Auto Parts in N.A.C.C.(North Automobile Components Company)

As after sales services become more and more popular,particularly preven­tive or corrective maintenance,the intervention and repair of the custom­er’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers.The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs.The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain.This reuse is primarily driven by extending the economic life of goods,initially re­garded as waste and therefore without added value,by transforming them into valuable spare parts that can be reused;secondly,for environmental or regulatory reasons,demanding responsibility for the treatment of products at the end of their life;and thirdly,to improve the availability of parts for maintenance,especially parts that the organization can no longer purchase or that are impacted by other issues.It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition.In this paper,we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts,identify the thresh­olds of inventory management policies,and finally,compare the different policies by service level and inventory level performance for the N.A.C.C.company.

Charting the future:Intelligent and connected vehicles reshaping the bus system

1 Introduction Driven by technological innovation and digital evolution,the current automotive industry is standing at the cusp of a transformative era(Liu et al.,2023).As urban centers continue to expand and intensify the demands on transportation networks,the need for solutions to alleviate congestion,boost traffic efficiency,and enhance road safety becomes increasingly urgent.On this occasion,intelligent and connected vehicles,integrating vehicles,infrastructure,and cloud computing,promise a smarter mode of passenger transportation and pave the way for a more interconnected and responsive urban transit ecosystem(Cao et al.,2023).Therefore,traditional passenger buses are on the verge of significant transformation in terms of their functional technologies and operational models.This will bring about a host of benefits such as higher efficiency,better passenger experiences,and safer road environments.This paper provides a comprehensive outlook on intelligent and connected passenger buses(ICPBs),delving into the integrated vehicle-road-cloud platform and highlighting the key technologies that will shape the future bus system.As illustrated in Fig.1,it showcases the key perspectives on the future of ICPBs.

SceGAN:A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on highways.An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage.Experiments showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,respectively.The coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.

A deep learning method for traffic light status recognition

Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

With the development of connected and automated vehicles(CAVs),forming strategies could extend from the typically used first-come-first-served rules.It is necessary to consider passing priorities when crossing intersections to prevent conflicts.In this study,a hierarchical strategy based on a cooperative game was developed to improve safety and efficiency during right-turning merging.A right-turn merging conflict model was established to analyze the right-turning vehicle characteristics of the traffic flow.The proposed three-layered hierarchical strategy includes a decision-making layer,a task layer,and an operation layer.A decision-making-layer cooperative game strategy was used to determine the merging priority of straight-going traffic and right-turning flows.In addition,a task-layer cooperative game strategy was designed for the merging sequence.A modified consensus algorithm was utilized to optimize the speed of vehicles in the virtual platoon of the operation layer.Traffic simulations were performed on the PYTHON-SUMO integrated platform to verify the proposed strategy.The simulation results show that,compared with other methods,the proposed hierarchical strategy has the shortest travel time and loss time and performs better than other methods when the straight-going traffic flow increases during right-turning merging at the intersection.The proposed method shows superiority under a significant traffic flow with a threshold of 900 vehicle/(h·lane).This satisfactory application of right-turning merging might be extended to ramps,lane-changing,and other scenarios in the future.

Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation:A review

Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.

Flow latency analysis of component-based embedded software:Model and methodology

A wide variety of ways to analyze the end-to-end latency emerges due to the feature of component-based software.The researchers began to see that the latency is more sensitive to the data and control flows than the software architecture.However,for an embedded software,the latency depends upon the hardware heavily.To illuminate the feature clearly,we extend the atomic model of component-based software first.A way to specify the flows involved is further developed to identify the end-to-end latency.What is more,a novel methodology that bridges the gap between a constraint on latency and an execution platform is proposed for the embedded software.By constructing a hierarchical architecture,it is available to consider the methodology as a decision problem where the satisfiability module theory(SMT)can be applied.Experimental results demonstrate how the latency analysis conducts with the proposed model and methodology for the complex software architecture.

Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments

Purpose–The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.Design/methodology/approach–Field tests with 17 participants were conducted in the connected and automated vehicle test field.All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed.Demographic data,vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit,such as electrocardiograph for heart rate,electromyography for muscle strength,electrodermal activity for skin conductance and force-sensing resistor for braking pressure.Findings–This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs.The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.Originality/value–The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time.Therefore,the driver states in distracted driving are significantly different than in regular driving,the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics.To the best of the authors’knowledge,this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

Precise vehicle ego-localization using feature matching of pavement images

Purpose–Precise vehicle localization is a basic and critical technique for various intelligent transportation system(ITS)applications.It also needs to adapt to the complex road environments in real-time.The global positioning system and the strap-down inertial navigation system are two common techniques in thefield of vehicle localization.However,the localization accuracy,reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding,vision enhancement and automatic parking.Aiming at the problems above,this paper aims to propose a precise vehicle ego-localization method based on image matching.Design/methodology/approach–This study included three steps,Step 1,extraction of feature points.After getting the image,the local features in the pavement images were extracted using an improved speeded up robust features algorithm.Step 2,eliminate mismatch points.Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust.Step 3,matching of feature points and trajectory generation.Findings–Through the matching and validation of the extracted local feature points,the relative translation and rotation offsets between two consecutive pavement images were calculated,eventually,the trajectory of the vehicle was generated.Originality/value–The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.